Publications

Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve?
Fırat Öncel
Matthias Bethge
Beyza Ermis
Mirco Ravanaelli
Yusuf Cem Sübakan
cCaugatay Yildiz
2023-12-31
EMNLP (publié)
doi.org
arxiv.org
An Addendum to NeBula: Toward Extending Team CoSTAR’s Solution to Larger Scale Environments
Ali Agha
Kyohei Otsu
Benjamin Morrell
David D. Fan
Sung-Kyun Kim
Muhammad Fadhil Ginting
Xianmei Lei
Jeffrey Edlund
Seyed Fakoorian
Amanda Bouman
Fernando Chavez
Taeyeon Kim
Gustavo J. Correa
Maira Saboia
Angel Santamaria-Navarro
Brett Lopez
Boseong Kim
Chanyoung Jung
Mamoru Sobue
Oriana Claudia Peltzer … (voir 69 de plus)
Joshua Ott
Robert Trybula
Thomas Touma
Marcel Kaufmann
Tiago Stegun Vaquero
Torkom Pailevanian
Matteo Palieri
Yun Chang
Andrzej Reinke
Matthew Anderson
Frederik E.T. Schöller
Patrick Spieler
Lillian Clark
Avak Archanian
Kenny Chen
Hovhannes Melikyan
Anushri Dixit
Harrison Delecki
Daniel Pastor
Barry Ridge
Nicolas Marchal
Jose Uribe
Sharmita Dey
Kamak Ebadi
Kyle Coble
Alexander Nikitas Dimopoulos
Vivek Thangavelu
Vivek Shankar Vardharajan
Nicholas Palomo
Antoni Rosinol
Arghya Chatterjee
Christoforos Kanellakis
Bjorn Lindqvist
Micah Corah
Kyle Strickland
Ryan Stonebraker
Michael Milano
Christopher E. Denniston
Sami Sahnoune
Thomas Claudet
Seungwook Lee
Gautam Salhotra
Edward Terry
Rithvik Musuku
Robin Schmid
Tony Tran
Ara Kourchians
Justin Schachter
Hector Azpurua
Levi Resende
Arash Kalantari
Jeremy Nash
Josh Lee
Christopher Patterson
Jen Blank
Kartik Patath
Yuki Kubo
Ryan Alimo
Yasin Almalioglu
Aaron Curtis
Jacqueline Sly
Tesla Wells
Nhut T. Ho
Mykel Kochenderfer
Giovanni Beltrame
George Nikolakopoulos
David Shim
Luca Carlone
Joel Burdick
2023-12-31
IEEE Transactions on Field Robotics (publié)
doi.org
arxiv.org
Affirmative Safety: An Approach to Risk Management for Advanced Ai
Akash Wasil
Joshua Clymer
David M. Krueger
Emily Dardaman
Simeon Campos
Evan Murphy
2023-12-31
SSRN Electronic Journal (publié)
doi.org
AmbieGen at the SBFT 2024 Tool Competition - CPS-UAV Track
Dmytro Humeniuk
Foutse Khomh
2023-12-31
SBFT@ICSE (publié)
doi.org
AmbieGenVAE at the SBFT 2024 Tool Competition - Cyber-Physical Systems Track
Dmytro Humeniuk
Foutse Khomh
2023-12-31
SBFT@ICSE (publié)
doi.org
An Analysis of Quantile Temporal-Difference Learning
Mark Rowland
Remi Munos
Mohammad Gheshlaghi Azar
Yunhao Tang
Georg Ostrovski
Anna Harutyunyan
K. Tuyls
Bellemare Marc-Emmanuel
Will Dabney
We analyse quantile temporal-difference learning (QTD), a distributional reinforcement learning algorithm that has proven to be a key compon… (voir plus)ent in several successful large-scale applications of reinforcement learning. Despite these empirical successes, a theoretical understanding of QTD has proven elusive until now. Unlike classical TD learning, which can be analysed with standard stochastic approximation tools, QTD updates do not approximate contraction mappings, are highly non-linear, and may have multiple fixed points. The core result of this paper is a proof of convergence to the fixed points of a related family of dynamic programming procedures with probability 1, putting QTD on firm theoretical footing. The proof establishes connections between QTD and non-linear differential inclusions through stochastic approximation theory and non-smooth analysis.
2023-12-31
Journal of Machine Learning Research (publié)
doi.org
arxiv.org
An Analytic Hierarchy Process based approach for assessing the performance of photovoltaic solar power plants
Meryam Chafiq
Loubna Benabbou
Hanane Dagdougui
Ismail Belhaj
Abdelali Djdiaa
Hicham Bouzekri
Abdelaziz Berrado
2023-12-31
IFAC-PapersOnLine (publié)
doi.org
Application-Driven Innovation in Machine Learning
David Rolnick
Alán Aspuru-Guzik
Sara Beery
Bistra Dilkina
Priya L. Donti
Marzyeh Ghassemi
Hannah Kerner
Claire Monteleoni
Esther Rolf
Milind Tambe
Adam White
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly i… (voir plus)mportant. Such work offers the potential for significant impact not merely in domains of application but also in machine learning itself. In this paper, we describe the paradigm of application-driven research in machine learning, contrasting it with the more standard paradigm of methods-driven research. We illustrate the benefits of application-driven machine learning and how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation. We outline how these processes may be improved.
2023-12-31
ICML (publié)
doi.org
arxiv.org
Arbitrary methodological decisions skew inter-brain synchronization estimates in hyperscanning-EEG studies
Marius Zimmermann
Kathrine Schultz-Nielsen
Guillaume Dumas
Ivana Konvalinka
Over the past decade, hyperscanning has emerged as an important methodology to study neural processes underlying human interaction using fMR… (voir plus)I, EEG, fNIRS, and MEG. However, many methodological decisions regarding preprocessing and analysis of hyperscanning data have not yet been standardized in the hyperscanning community, yet may affect inter-brain estimates. Here we systematically investigate the effects common methodological choices can have on estimates of phase-based inter-brain synchronization (IBS) measures, using real and simulated hyperscanning (dual) EEG data. Notably, we introduce a new method to compute circular correlation (CCorr) coefficients in IBS studies, which performs more reliably in comparison to the standard approach, showing that the conventional CCorr implementation leads to large fluctuations in IBS estimates due to fluctuations in circular mean directions. Furthermore, we demonstrate how short epoch durations (of 1 second or less) can lead to inflated IBS estimates in scenarios with no strong underlying interaction. Finally, we show how signal-to-noise ratios and temporal factors may confound IBS estimates, particularly when comparing e.g., resting states with conditions involving motor actions. For each of these investigated effects, we provide recommendations for future research employing hyperscanning-EEG techniques, aimed at increasing validity and replicability of inter-brain synchronization studies.
2023-12-31
Imaging Neuroscience (publié)
doi.org
Are LLMs Breaking MT Metrics? Results of the WMT24 Metrics Shared Task
Markus Freitag
Nitika Mathur
Daniel Deutsch
Chi-kiu Lo
Eleftherios Avramidis
Ricardo Rei
Brian Thompson
Frédéric Blain
Tom Kocmi
Jiayi Wang
David Ifeoluwa Adelani
Marianna Buchicchio
Chrysoula Zerva
2023-12-31
Conference on Machine Translation (publié)
doi.org
ARGV: 3D genome structure exploration using augmented reality
Chrisostomos Drogaris
Yanlin Zhang
Eric Zhang
Elena Nazarova
Roman Sarrazin-Gendron
Sélik Wilhelm-Landry
Yan Cyr
Jacek Majewski
Mathieu Blanchette
Jérôme Waldispühl
Over the past two decades, scientists have increasingly realized the importance of the three-dimensional (3D) genome organization in regulat… (voir plus)ing cellular activity. Hi-C and related experiments yield 2D contact matrices that can be used to infer 3D models of chromosome structure. Visualizing and analyzing genomes in 3D space remains challenging. Here, we present ARGV, an augmented reality 3D Genome Viewer. ARGV contains more than 350 pre-computed and annotated genome structures inferred from Hi-C and imaging data. It offers interactive and collaborative visualization of genomes in 3D space, using standard mobile phones or tablets. A user study comparing ARGV to existing tools demonstrates its benefits.
2023-12-31
BMC Bioinformatics (publié)
doi.org
AsmDocGen: Generating Functional Natural Language Descriptions for Assembly Code
Jesia Yuki
Mohammadhossein Amouei
Benjamin C. M. Fung
Philippe Charland
Andrew Walenstein
2023-12-31
International Conference on Software and Data Technologies (publié)
doi.org